Abstract
This paper proposes a novel framework of multi-hypothesis compressed video sensing. Multi-hypothesis prediction and bi-directional motion estimation are applied to generate side information candidates. The correlation coefficients between the non-key frame and the three candidates are calculated respectively for selecting the most similar side information. The simulation results show that the proposed framework can provide better recovery performance than the framework using original MH-BCS-SPL algorithm.
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CITATION STYLE
Chen, R., Tong, Y., Yang, J., & Wu, M. (2018). Compressed video sensing with multi-hypothesis prediction. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 6, pp. 489–496). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-319-59463-7_48
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